As pointed in [1]} or [2]}, in general, a multi-agent system may not originally a networked evolutionary game. There is a system level objective function \(\phi :S\rightarrow {\mathbb {R}}\)  that a system designer seeks to maximize. Then we may design a set of suitable utility functions such that the system becomes a potential game with \(\phi \)  as its potential function. This is the first task in game theoretic approach, which is described in top part of Fig. REF . Second task is: since the information used is a local one, a learning SUR is necessarily to assure the system converges to the maximum object value. We start with a motivated example.
